References of "Sougné, Jacques"
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See detailBinding Problem
Sougné, Jacques ULg

in Nadel, L. (Ed.) Encyclopedia of Cognitive Science (2003)

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See detailShort Term Memory in a Network of Spiking Neurons
Sougné, Jacques ULg; Bullinaria; Lowe

in Bullinaria, John A.; Lowe, W. (Eds.) Connectionist Models of Cognition and Perception (2002)

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See detailA learning algorithm for synfire chains
Sougné, Jacques ULg

in French, R.; Sougné, Jacques (Eds.) Connectionist models of learning : Development and Evolution (2001)

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See detailSynfire chains and catastrophic interference
Sougné, Jacques ULg; French, R.

in Moore, J. D. (Ed.) Proceedings of the twenty-third annual conference of the Cognitive Science Society : 1-4 august 2001 : University of Edinburgh (2001)

The brain must be capable of achieving extraordinarily precise sub-milisecond timing with imprecise neural hardware. We discuss how this might be possible using synfire chains (Abeles, 1991) and present a ... [more ▼]

The brain must be capable of achieving extraordinarily precise sub-milisecond timing with imprecise neural hardware. We discuss how this might be possible using synfire chains (Abeles, 1991) and present a synfire chain learning algorithm for a sparsely-distributed network of spiking neurons (Sougné, 1999). Surprisingly, we show that this learning is not subject to catastrophic interference, a problem that plagues many standard connectionist networks. We show that the forgetting of synfire chains in this type of network closely resembles the classing forgetting pattern described by Barnes [less ▲]

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See detailBinding and multiple instantiation in a distributed network of spiking nodes
Sougné, Jacques ULg

in Connection Science (2001), 13(2), 99-126

An implementation of a distributed connectionist network of spiking neuron-like elements is presented. Spiking nodes fire at a precise moment and transmit their activation, with particular strenghts and ... [more ▼]

An implementation of a distributed connectionist network of spiking neuron-like elements is presented. Spiking nodes fire at a precise moment and transmit their activation, with particular strenghts and delays, to nodes connected to them. Wen the potential of the node reaches a particular threshold, it emits a spike. Thereafter, the potential is reset to a resting value. The receiving nodes accumulate potential, but also, slowly lose their potential through decay. As with real neurons, after firing there is a short refractory period during which the node will be completely insensitive to incoming signals, after which its sensitivity will slowly increase. Precise timing properties are used to represent symbols in a distributed manner and also to solve the problems of variable binding and multiple instantiation. Several predictions about human short-term memory, predicate processing, complex reasoning and multiple instantiation arise from this model. This network shows how symbolic processing can be achieved using neurobiologically and psychologically plausible mechanisms that also have the advantages of generalization and noise tolerance found in connectionist networks. [less ▲]

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See detailSimulating conditional reasoning containing negations : A computer model and human data
Sougné, Jacques ULg

in Gleitman, L. R. (Ed.) Proceedings of the twenty-second annual Conference of the Cognitive Science Society : August 13-15, 2000 (2000)

Modelling human conditioning reasoning of the type 'if p then q' containing negations poses a challenge for connectionism. A network of spiking neurons (INFERNET) was used to model this type of ... [more ▼]

Modelling human conditioning reasoning of the type 'if p then q' containing negations poses a challenge for connectionism. A network of spiking neurons (INFERNET) was used to model this type of conditional reasoning. This model also provides insights on certain human limitations. The model is compared to empirical data, and classical explanations. Statistical analysis shows that the model's performance not only surpasses classical explanations but also provides a very good overall fit to empirical data. INFERNET simulator results also compared to human performance. The simulations compare well with both human performance and limitations. [less ▲]

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See detailINFERNET : A neurocomputational model of binding and inference
Sougné, Jacques ULg

Doctoral thesis (1999)

We know that color and form are processed in distinct areas of the human brain. This information must somehow be brought together. How the brain might achieve these colorform associations, as well as all ... [more ▼]

We know that color and form are processed in distinct areas of the human brain. This information must somehow be brought together. How the brain might achieve these colorform associations, as well as all other associations of this type, is one of the central themes of this dissertation. When looking at a field of poppies on a sunny day, how can we correctly associate the color red with the poppies, green with the grass, and blue with the sky, while avoiding associating the color red with the grass and the color blue with poppies? How can we associate the perception of red poppies with the name “red poppy,” and with its superordinate category “flower?” A red poppy is composed of several features, like its shape, color, texture, etc. How might a cognitive system bind these features to build a coherent whole? If we see Louise picking a red poppy, how can we correctly associate Louise with the picker and the red poppy with the picked object, without making the opposite and incorrect association? These associations may seem easy to us, but how does the brain achieve them? How a cognitive system binds a set of features together, associates a filler with a role, a value with a variable, an attribute with a concept, ... is what we mean by “the binding problem.” This thesis focuses on the neurobiological processes that enable connectionist cognitive systems to display binding abilities, on the constraints that affect the binding process, and on the cognitive consequences of these constraints. To study these processes, we developed a computer model of them. This method forces a detailed and unequivocal description of processes used by the simulation. This method is also a powerful means of generating new hypotheses. In this study we attempt to link psychological processes with the neuronal constraints that act on brain functioning. The brain is composed of approximately 10 billion highly interconnected neurons. To achieve binding it is necessary for neurons to communicate with each other because it has been shown that different aspects of a perceived object are not processed in the same cortical areas. Therefore, there must be a means for binding neurons responding to each of these different aspects. The neurons responding to the color red, to the object’s shape, and to its name must be linked to produce a coherent whole representing the red poppy. Neurons are connected by synapses. The functioning of these connections is constrained by the architecture of the brain and by the process of signal transmission. A particular neuron is connected to a relatively small set of other neurons. Therefore, communication between any two neurons generally requires a chain of transmission through intermediate neurons. A pre-synaptic neuron has an effect on another neuron (called the post-synaptic neuron) only if the pre-synaptic neuron emits an action potential (i.e., if it fires). As a consequence, this brief polarization, which last a few milliseconds, results in a modification of other neurons' firing potential. Transmission efficiency depends on the strength of the connecting synapses and the state of the post-synaptic neuron. When a neuron emits an action potential, it is completely insensitive to incoming signals for a short period, then its sensitivity slowly increases. A single pre-synaptic cortical neuron cannot alone provoke the post-synaptic neuron firing. This post-synaptic neuron must receive convergent and more or less synchronized signals from many synapses in order to fire. These neurobiological properties of neurons and neuronal firing constrain the way in which the brain can achieve binding. Among the various hypotheses of how this could be done, we chose synchronization of action potentials for our model. In the red poppy example, neurons responding to the color red will fire in synchrony with those responding to the shape of the flower and to the name “red poppy.” This particular synchronized cluster corresponding to “red poppy” must be temporally distinguished from the cluster responding to “green grass.” Numerous neurobiological studies seem to confirm this action-potential synchrony hypothesis. They show that synchronization involves a particular timing precision and occurs at a particular oscillation frequency. This oscillation requires participating neurons to fire repeatedly and rhythmically for a particular period of time. These properties of firing timing and duration have been implemented in a computer simulation called INFERNET. This artificial neural network uses integrate-and-fire nodes (artificial neuronlike elements). These nodes fire at a precise moment and transmit their activation, with a particular strength and delay, to nodes connected to them. When the potential of the node reaches a particular threshold, it emits a spike. Thereafter, the potential is reset to a resting value. As with real neurons, this node will then be completely insensitive to incoming signals for a short period, after which its sensitivity will slowly increase. INFERNET solves the binding problem by means of oscillation synchrony. Symbols are represented by clusters of nodes firing in synchrony. Fillers are also bound to their roles by synchrony. This synchronous activity defines a window of synchrony i.e., an interval during which the required nodes fire. This time interval takes neurally plausible values. Object discrimination is achieved by a succession of windows of synchrony. Bindings are maintained in memory by the use of particular oscillations. The rhythmic activity and the synchrony precision constrain the number of distinct entities that the system is able to maintain in memory. This represents the short term memory span of INFERNET. We show that this span is comparable with human short term memory span. The limited number of windows of synchrony also constrains predicate representations. This prediction is tested on human participants. If there are too many windows of synchrony, these will interfere with each other. In addition, binding strength decreases with time. These two properties explain why the short-term memory of INFERNET displays primacy and recency effects similar to those observed in humans. Bindings in INFERNET are also constrained by the number of intermediate steps required for particular role nodes to enter into synchrony with the filler nodes. This constraint is shown to provided a plausible explanation of various differences human reasoning. The last INFERNET constraint concerns multiple instantiation. This problem arises in connectionist networks as soon as a symbol has to be simultaneously used twice in different ways. Since INFERNET’s short term memory is the transient activation of parts of long term memory, it cannot make multiple copies of a symbol, in the same way, for example, that a symbolic system does. The INFERNET solution to the multiple instantiation problem involves superposition of different node oscillations. This process is constrained by the refractory period of the nodes. A number of simulations with INFERNET and experiments on humans show that this solution is psychologically plausible. Multiple instantiation is also shown to be a plausible explanation of certain similarity effects in short term memory. INFERNET is also shown to be capable of symbolic processing with using neurologically and psychologically plausible mechanisms that have the advantages of generalization and noise tolerance found in connectionist networks. Finally, under certain circumstances, noise is shown to enhance INFERNET’s processing capabilities. [less ▲]

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See detailApport de l'intelligence artificielle à la psychologie
Defays, Daniel ULg; French, Robert; Sougné, Jacques ULg

in Rondal, Jean-Adolphe (Ed.) Introduction à la psychologie scientifique (1999)

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See detailConnectionist symbol processing : dead or alive ?
Blank, D. S.; Coltheart, M.; Diederich, J. et al

in Neural Computing Surveys (1999), 2

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See detailPeriod doubling as a means of representing multiply instantiated entities
Sougné, Jacques ULg

in Gernsbacher, Morton Ann; Derry, Sharon J. (Eds.) Proceedings of the Twentieth Annual Conference of the Cognitive Science Society (1998)

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See detailConnectionism and the problem of multiple instantiation
Sougné, Jacques ULg

in Trends in Cognitive Sciences (1998), 2(5), 183-189

Multiple instantiation is the ability to handle different instances of the same concept simultaneously. For example, from the following two facts: 'Pepin the Short was the son of Charles Martel' and ... [more ▼]

Multiple instantiation is the ability to handle different instances of the same concept simultaneously. For example, from the following two facts: 'Pepin the Short was the son of Charles Martel' and 'Charlemagne was the son of Pepin the Short', one can infer that Charles Martel was the grandfather of Charlemagne. This inference requires two instantiations of 'Pepin the Short', the first in the role of son, the second in the role of father. For a connectionist model that does not use a working area receiving copies of items from a long-term knowledge base, the problem of multiple instantiation is a particularly thorny one. People are able to deal with multiple instances, unlike most connectionist models, but nonetheless their performance when doing so is reduced. On the other hand, there is no decrease in performance for symbolic models doing multiple instantiation. A good cognitive model should reflect both human competence and human limitations. This review proposes several connectionist solutions to the problem of multiple instantiation and examines their merits. [less ▲]

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See detailA Neurobiologically Inspired Model of Working Memory Based on Neuronal Synchrony and Rythmicity
Sougné, Jacques ULg; French, R. M.

in Bullinaria, J.A.; Glasspool, D.W.; Houghton, G. (Eds.) Proceedings of the Fourth Neural Computation and Psychology Workshop: Connectionist Representations (1997)

The connectionist model of reasoning presented here, INFERNET, implements a working memory that is the activated part of long-term memory. This is achieved by making use of temporal properties of the node ... [more ▼]

The connectionist model of reasoning presented here, INFERNET, implements a working memory that is the activated part of long-term memory. This is achieved by making use of temporal properties of the node spikes. A particular solution of the problem of multiple instantiation is proposed. This model makes predictions that have been tested experimentally and the results of these experiments are reported here. These results would seem to challenge modular models of memory. [less ▲]

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See detailVariable Oscillation Frequencies for Solving the Problem of Multiple Instantiation
Sougné, Jacques ULg; Shafto, M. G.; Langley, P.

in Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society (1997)

Distributed connectionists models of reasoning must solve the problem of multiple instantiation for two reasons. First, reasoning can involve two or more instantiations of the same predicate or object ... [more ▼]

Distributed connectionists models of reasoning must solve the problem of multiple instantiation for two reasons. First, reasoning can involve two or more instantiations of the same predicate or object. Second, in a distributed representation, two closely related concepts must share common resources or nodes. Reasoning with these two concepts requires that nodes pertaining to them be instantiated twice. This paper presents a model (INFERNET) that uses temporal synchrony variable binding. It proposes a particular solution to the problem of multiple instantiation that involves the use of different oscillation frequencies. This solution implies some predictions. These predictions are tested on human participants, and the results are presented here. They confirm model predictions. [less ▲]

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See detailA connectionist model of reflective reasoning using temporal properties of none firing
Sougné, Jacques ULg

in Cottrell (Ed.) Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society (1996)

This paper presents a connectionist model of human reasoning that uses temporal relations between node firing. Temporal synchrony is used for representing variable binding and concepts. Temporal ... [more ▼]

This paper presents a connectionist model of human reasoning that uses temporal relations between node firing. Temporal synchrony is used for representing variable binding and concepts. Temporal succession serves to represent rules by linking antecedent to consequent parts of the rule. The number of successive synchronies is affected by two well-known neurobiological parameters, the frequency of neural rythmic activity and the precision of neural synchronization. Reasoning is predicted to be constrained by these variables. An experiment manipulating the amount of successive synchronies is presented. Experimental results would seem to confirm the predictions. [less ▲]

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See detailA connectionist model of reflective reasoning using temporal properties of nodes firing
Sougné, Jacques ULg; Cottrell, G. W.

in Proceedings of the Eighteenth Annual conference of the Cognitive Science Society (1996)

This paper presents a connectionist model of human reasoning that uses temporal relations between node firing. Temporal synchrony is used for representing variable binding and concepts. Temporal ... [more ▼]

This paper presents a connectionist model of human reasoning that uses temporal relations between node firing. Temporal synchrony is used for representing variable binding and concepts. Temporal succession serves to represent rules by linking antecedent to consequent parts of the rule. The number of successive synchronies is affected by two well-known neurobiological parameters, the frequency of neural rythmic activity and the precision of neural synchronization. Reasoning is predicted to be constrained by these variables. An experiment manipulating the amount of successive synchronies is presented. Experimental results would seem to confirm the predictions. [less ▲]

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See detailLe raisonnement temporel
Sougné, Jacques ULg

in Cellier, J. M. (Ed.) Gestion du temps dans les environnements dynamiques (1996)

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See detailA connectionist model of reflective reasoning using temporal properties of node firing
Sougné, Jacques ULg

in Proceedings of the Annual Meetingof the Belgian Psychological Society (1996)

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See detailTEMPORAL REASONING AND REASONING THEORIES A CASE-STUDY IN ANESTHESIOLOGY
Sougné, Jacques ULg; Nyssen, Anne-Sophie ULg; De Keyser, Véronique ULg

in Psychologica Belgica (1993), 33(2), 311-328

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See detailTemporal reasoning and reasoning theories : a case study in anaesthesiology
Sougné, Jacques ULg; Nyssen, Anne-Sophie ULg; De Keyser, Véronique ULg

in Psychologica Belgica (1993), 33(2), 311-328

The purpose of this article is to clarify the theoritical base necessary for the design of a computer-based simulation of temporal reasoning. Simulation allows a better understanding of phenomena that ... [more ▼]

The purpose of this article is to clarify the theoritical base necessary for the design of a computer-based simulation of temporal reasoning. Simulation allows a better understanding of phenomena that appear in working situations and even, understanding and preventing some human errors. In order ot illustrate theoritical concepts, we will provide examples of a situation in which the dynamic character of the evolution, the critical character of the planning and of the synchronization force temporal reasoning. This situation will be that of anaesthesia. [less ▲]

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